Machine Learning in Hypertrophic Cardiomyopathy: Nonlinear Model From Clinical and CMR Features Predicting Cardiovascular Events.

BACKGROUND: The cumulative burden of hypertrophic cardiomyopathy (HCM) is significant, with a noteworthy percentage (10%-15%) of patients with HCM per year experiencing major adverse cardiovascular events (MACEs). A current risk stratification scheme for HCM had only limited accuracy in predicting sudden cardiac death (SCD) and failed to account for a broader spectrum of adverse cardiovascular events and cardiac magnetic resonance (CMR) parameters. OBJECTIVES: This study sought to develop and evaluate a machine learning (ML) framework that integrates CMR imaging and clinical characteristics to predict MACEs in patients with HCM. METHODS: A total of 758 patients with HCM (67% male; aged 49 ± 14 years) who were admitted between 2010 and 2017 from 4 medical centers were included. The ML model was built on the internal discovery cohort (533 patients with HCM, admitted to Fuwai Hospital, Beijing, China) by using the light gradient-boosting machine and internally evaluated using cross-validation. The external test cohort consisted of 225 patients with HCM from 3 medical centers. A total of 14 CMR imaging features (strain and late gadolinium enhancement [LGE]) and 23 clinical variables were evaluated and used to inform the ML model. MACEs included a composite of arrhythmic events, SCD, heart failure, and atrial fibrillation-related stroke. RESULTS: MACEs occurred in 191 (25%) patients over a median follow-up period of 109.0 months (Q1-Q3: 73.0-118.8 months). Our ML model achieved areas under the curve (AUCs) of 0.830 and 0.812 (internally and externally, respectively). The model outperformed the classic HCM Risk-SCD model, with significant improvement (P < 0.001) of 22.7% in the AUC. Using the cubic spline analysis, the study showed that the extent of LGE and the impairment of global radial strain (GRS) and global circumferential strain (GCS) were nonlinearly correlated with MACEs: an elevated risk of adverse cardiovascular events was observed when these parameters reached the high enough second tertiles (11.6% for LGE, 25.8% for GRS, -17.3% for GCS). CONCLUSIONS: ML-empowered risk stratification using CMR and clinical features enabled accurate MACE prediction beyond the classic HCM Risk-SCD model. In addition, the nonlinear correlation between CMR features (LGE and left ventricular pressure gradient) and MACEs uncovered in this study provides valuable insights for the clinical assessment and management of HCM.

Association between enlarged perivascular spaces in basal ganglia and cerebral perfusion in elderly people.

Background and objective: Enlarged perivascular spaces in basal ganglia (BG-EPVS) are considered an imaging marker of cerebral small vessel disease (CSVD), but its pathogenesis and pathophysiological process remain unclear. While decreased cerebral perfusion is linked to other CSVD markers, the relationship between BG-EPVS and cerebral perfusion remains ambiguous. This study aimed to explore this association. Methods: Elderly individuals with severe BG-EPVS (n = 77) and age/sex-matched controls (n = 89) underwent head CT perfusion imaging. The cerebral perfusion parameters including mean transit time (MTT), time to maximum (TMAX), cerebral blood flow (CBF), and cerebral blood volume (CBV) were quantitatively measured by symmetric regions of interest plotted in the basal ganglia region. Point-biserial correlation and logistics regression analysis were performed to investigate the association between BG-EPVS and cerebral perfusion. Results: There were no significant differences in MTT, TMAX, or CBF between BG-EPVS group and control group. CBV was significantly lower in the BG-EPVS group (p = 0.035). Point-biserial correlation analysis showed a negative correlation between BG-EPVS and CBV (r = -0.198, p = 0.011). BG-EPVS group and control group as the dependent variable, binary logistics regression analysis showed that CBV was not an independent risk factor for severe BG-EPVS (p = 0.448). All enrolled patients were divided into four groups according to the interquartile interval of CBV. The ordered logistic regression analysis showed severe BG-EPVS was an independent risk factor for decreased CBV after adjusting for confounding factors (OR = 2.142, 95%CI: 1.211-3.788, p = 0.009). Conclusion: Severe BG-EPVS is an independent risk factor for decreased CBV in the elderly, however, the formation of BG-EPVS is not solely dependent on changes in CBV in this region. This finding provides information about the pathophysiological consequence caused by severe BG-EPVS.

Fragmentation Patterns of Phenolic C-Glycosides in Mass Spectrometry Analysis.

BACKGROUND: Many phenolic C-glycosides possess nutritional benefits and pharmacological efficacies. However, the MS/MS fragmentation pattern of phenolic C-glycosides analysis is understudied. This paper aims to determine the MS/MS fragmentation patterns of phenolic C-glycosides. METHOD: Ten compounds with different sugar moieties, aglycones, and substitutes were analyzed to determine the impact of these structural features on MS/MS fragmentation using UPLC-QTOF-MS analysis. RESULTS: The results showed that water loss followed by RDA reaction and alpha cleavage in the C-C bonded sugar moieties are the major fragmentation pathways. Additionally, the sugar cleavage was not affected by the skeleton and the substitute of the aglycones. These results suggested that the fragmentation patterns of phenolic C-glycosides differ from those in the O-glycosides, where the O-C glycosidic bond is the most cleavage-liable bond in MS/MS analysis. CONCLUSIONS: These MS/MS fragmentation patterns can be used for the identification of C-glycosides from dietary components and herbal medicine as well as developing robust methods using MRM methods to quantify C-glycosides.

Joint diffusion: mutual consistency-driven diffusion model for PET-MRI co-reconstruction.

Objective. Positron Emission Tomography and Magnetic Resonance Imaging (PET-MRI) systems can obtain functional and anatomical scans. But PET suffers from a low signal-to-noise ratio, while MRI are time-consuming. To address time-consuming, an effective strategy involves reducing k-space data collection, albeit at the cost of lowering image quality. This study aims to leverage the inherent complementarity within PET-MRI data to enhance the image quality of PET-MRI.Approach. A novel PET-MRI joint reconstruction model, termed MC-Diffusion, is proposed in the Bayesian framework. The joint reconstruction problem is transformed into a joint regularization problem, where data fidelity terms of PET and MRI are expressed independently. The regular term, the derivative of the logarithm of the joint probability distribution of PET and MRI, employs a joint score-based diffusion model for learning. The diffusion model involves the forward diffusion process and the reverse diffusion process. The forward diffusion process adds noise to transform a complex joint data distribution into a known joint prior distribution for PET and MRI simultaneously, resembling a denoiser. The reverse diffusion process removes noise using a denoiser to revert the joint prior distribution to the original joint data distribution, effectively utilizing joint probability distribution to describe the correlations of PET and MRI for improved quality of joint reconstruction.Main results. Qualitative and quantitative improvements are observed with the MC-Diffusion model. Comparative analysis against LPLS and Joint ISAT-net on the ADNI dataset demonstrates superior performance by exploiting complementary information between PET and MRI. The MC-Diffusion model effectively enhances the quality of PET and MRI images.Significance. This study employs the MC-Diffusion model to enhance the quality of PET-MRI images by integrating the fundamental principles of PET and MRI modalities and leveraging their inherent complementarity. Furthermore, utilizing the diffusion model to learn the joint probability distribution of PET and MRI, thereby elucidating their latent correlation, facilitates a more profound comprehension of the priors obtained through deep learning, contrasting with black-box prior or artificially constructed structural similarities.

Deep learning for intracranial aneurysm segmentation using CT angiography.

OBJECTIVE: This study aimed to employ a two-stage deep learning method to accurately detect small aneurysms (4-10 mm in size) in computed tomography angiography (CTA) images. Methods: This study included 956 patients from 6 hospitals and a public dataset obtained with 6 CT scanners from different manufacturers. The proposed method consists of two components: a lightweight and fast head region selection (HRS) algorithm and an adaptive 3D nnU-Net network, which is used as the main architecture for segmenting aneurysms. Segments generated by the deep neural network were compared with expert-generated manual segmentation results and assessed using Dice scores. Results: The area under the curve (AUC) exceeded 79% across all datasets. In particular, the precision and AUC reached 85.2% and 87.6%, respectively, on certain datasets. The experimental results demonstrated the promising performance of this approach, which reduced the inference time by more than 50% compared to direct inference without HRS. Conclusion: Compared with a model without HRS, the deep learning approach we developed can accurately segment aneurysms by automatically localizing brain regions and can accelerate aneurysm inference by more than 50%. Keywords: Deep Learning, CT Angiography, Aneurisms.

Volatile metabolomics and transcriptomics analyses provide insights into the mechanism of volatile changes during fruit development of 'Ehime 38' (Citrus reticulata) and its bud mutant.

Volatile compounds are important determinants affecting fruit flavor. Previous study has identified a bud mutant of 'Ehime 38' (Citrus reticulata) with different volatile profile. However, the volatile changes between WT and MT during fruit development and underlying mechanism remain elusive. In this study, a total of 35 volatile compounds were identified in the pulps of WT and MT at five developmental stages. Both varieties accumulated similar and the highest levels of volatiles at stage S1, and showed a downward trend as the fruit develops. However, the total volatile contents in the pulps of MT were 1.4-2.5 folds higher than those in WT at stages S2-S5, which was mainly due to the increase in the content of d-limonene. Transcriptomic and RT-qPCR analysis revealed that most genes in MEP pathway were positively correlated with the volatile contents, of which DXS1 might mainly contribute to the elevated volatiles accumulation in MT by increasing the flux into the MEP pathway. Moreover, temporal expression analysis indicated that these MEP pathway genes functioned at different developmental stages. This study provided comprehensive volatile metabolomics and transcriptomics characterizations of a citrus mutant during fruit development, which is valuable for fruit flavor improvement in citrus.

Tamarixetin ameliorates cerebral ischemia-reperfusion injury via suppressing nicotinamide adenine dinucleotide phosphate oxidase 2/nucleotide-binding oligomerization domain like receptor family pyrin domain-containing 3 inflammasome activation.

Tamarixetin, a natural dietary flavone, exhibits remarkable potential for the treatment of ischemic stroke. The present article aimed to explore the impact of tamarixetin on ischemic stroke and elucidate the underlying mechanisms. Effects of tamarixetin on ischemic stroke were evaluated in rats using the middle cerebral artery occlusion and reperfusion (MCAO/R) model, by assessing the neurological deficit scores, brain water content, brain infraction, and neuronal damage. The levels of proinflammatory cytokines, NLRP3 inflammasome activation, reactive oxygen species (ROS) production, and nicotinamide adenine dinucleotide phosphate (NADPH) oxidase expression were measured in MCAO/R rats and lipopolysaccharide-stimulated cells. Tamarixetin administration improved the neurological dysfunction and neuronal loss in MCAO/R rats. In addition, tamarixetin reduced microglial hyperactivation and proinflammatory cytokines expression in vivo and in vitro. Tamarixetin attenuated NF-κB p65 phosphorylation and promoter activity, reduced NLRP3 expression and caspase-1 cleavage, and downregulated IL-1ß and IL-18 secretions to suppress NLRP3 inflammasome activation. The levels of superoxide anion, hydrogen peroxide, and ROS were also suppressed by tamarixetin. The downregulation of NADP+ and NADPH levels, and gp91phox expression indicated the ameliorative effects of tamarixetin on NADPH oxidase activation. In the gp91phox knockdown cells treated with lipopolysaccharide, the effects of tamarixetin on NADPH oxidase activation, ROS generation, and NLRP3 inflammasome activation were diminished. Moreover, tamarixetin protects neurons against microglial hyperactivation in vitro. Our findings support the potential of tamarixetin as a therapeutic agent for ischemic stroke, and its mechanism of action involves the inhibition of NADPH oxidase-NLRP3 inflammasome signaling.

Synthesizing PET images from high-field and ultra-high-field MR images using joint diffusion attention model.

BACKGROUND: Magnetic Resonance Imaging (MRI) and Positron Emission Tomography (PET) stand as pivotal diagnostic tools for brain disorders, offering the potential for mutually enriching disease diagnostic perspectives. However, the costs associated with PET scans and the inherent radioactivity have limited the widespread application of PET. Furthermore, it is noteworthy to highlight the promising potential of high-field and ultra-high-field neuroimaging in cognitive neuroscience research and clinical practice. With the enhancement of MRI resolution, a related question arises: can high-resolution MRI improve the quality of PET images? PURPOSE: This study aims to enhance the quality of synthesized PET images by leveraging the superior resolution capabilities provided by high-field and ultra-high-field MRI. METHODS: From a statistical perspective, the joint probability distribution is considered the most direct and fundamental approach for representing the correlation between PET and MRI. In this study, we proposed a novel model, the joint diffusion attention model, namely, the joint diffusion attention model (JDAM), which primarily focuses on learning information about the joint probability distribution. JDAM consists of two primary processes: the diffusion process and the sampling process. During the diffusion process, PET gradually transforms into a Gaussian noise distribution by adding Gaussian noise, while MRI remains fixed. The central objective of the diffusion process is to learn the gradient of the logarithm of the joint probability distribution between MRI and noise PET. The sampling process operates as a predictor-corrector. The predictor initiates a reverse diffusion process, and the corrector applies Langevin dynamics. RESULTS: Experimental results from the publicly available Alzheimer's Disease Neuroimaging Initiative dataset highlight the effectiveness of the proposed model compared to state-of-the-art (SOTA) models such as Pix2pix and CycleGAN. Significantly, synthetic PET images guided by ultra-high-field MRI exhibit marked improvements in signal-to-noise characteristics when contrasted with those generated from high-field MRI data. These results have been endorsed by medical experts, who consider the PET images synthesized through JDAM to possess scientific merit. This endorsement is based on their symmetrical features and precise representation of regions displaying hypometabolism, a hallmark of Alzheimer's disease. CONCLUSIONS: This study establishes the feasibility of generating PET images from MRI. Synthesis of PET by JDAM significantly enhances image quality compared to SOTA models.

Updated global epidemiology atlas of human prion diseases.

Introduction: Human prion disease (PrD), a group of fatal and transmissible neurodegenerative diseases, consists of Creutzfeldt-Jakob disease (CJD), kuru, fatal familial insomnia (FFI), Gerstmann-Sträussler-Scheinker disease (GSS), and variably protease-sensitive prionopathy (VPSPr). The emergence of bovine spongiform encephalopathy (BSE) in cattle and variant CJD (vCJD) has greatly threatened public health, both in humans and animals. Since the 1990's, dozens of countries and territories have conducted PrD surveillance programs. Methods: In this study, the case numbers and alternative trends of different types of PrD globally and in various countries or territories from 1993 to 2020 were collected and analyzed based on the data from the websites of the international and national PrD surveillance programs, as well as from relevant publications. Results: The total numbers of the reported PrD and sporadic CJD (sCJD) cases in 34 countries with accessible annual case numbers were 27,872 and 24,623, respectively. The top seven countries in PrD cases were the USA (n = 5,156), France (n = 3,276), Germany (n = 3,212), Italy (n = 2,995), China (n = 2,662), the UK (n = 2,521), Spain (n = 1,657), and Canada (n = 1,311). The annual PrD case numbers and mortalities, either globally or in the countries, showed an increased trend in the past 27 years. Genetic PrD cases accounted for 10.83% of all reported PrD cases; however, the trend varied largely among the different countries and territories. There have been 485 iatrogenic CJD (iCJD) cases and 232 vCJD cases reported worldwide. Discussion: The majority of the countries with PrD surveillance programs were high- and upper-middle-income countries. However, most low- and lower-middle-income countries in the world did not conduct PrD surveillance or even report PrD cases, indicating that the number of human PrD cases worldwide is markedly undervalued. Active international PrD surveillance for both humans and animals is still vital to eliminate the threat of prion disease from a public health perspective.

Effect of compressive and tensile forces on glucose concentration and cell viability within the intervertebral disc: A finite element study.

Understanding the role of mechanical force on tissue nutrient transport is essential, as sustained force may affect nutrient levels within the disc and initiate disc degeneration. This study aims to evaluate the time-dependent effects of different compressive force amplitudes as well as tensile force on glucose concentration and cell viability within the disc. Based on the mechano-electrochemical mixture theory, a multiphasic finite element model of the lumbar intervertebral disc was developed. The minimum glucose concentration and minimum cell density in both normal and degenerated discs were predicted for different compressive force amplitudes, tensile force, and corresponding creep time. Under high compressive force, the minimum glucose concentration exhibited an increasing and then decreasing trend with creep time in the normal disc, whereas that of the degenerated disc increased, then decreased, and finally increased again. At steady state, a higher compressive force was accompanied by a lower glucose concentration distribution. In the degenerated disc, the minimum cell density was negatively correlated with creep time, with a greater range of affected tissue under a higher compressive force. For tensile force, the minimum glucose concentration of the degenerated disc raised over time. This study highlighted the importance of creep time, force magnitude, and force type in affecting nutrient concentration and cell viability. Sustained weight-bearing activities could deteriorate the nutrient environment of the degenerated disc, while tensile force might have a nonnegligible role in effectively improving nutrient levels within the degenerated disc.

Deuteration-Driven Photopharmacology: Deuterium-Labeled AzoCholine for Controlling Alpha 7 Nicotinic Acetylcholine Receptors.

Photopharmacology is a powerful approach to investigate biological processes and overcomes the common therapeutic challenges in drug development. Enhancing the photopharmacology properties of photoswitches contributes to extend their applications. Deuteration, a tiny structural modification, makes it possible to improve the photopharmacology and photophysical properties of prototype compounds, avoiding extra complex chemical changes or constructing multicomponent systems. In this work, we developed a series of D-labeled azobenzenes to expand the azobenzene photoswitchable library and introduced the D-labeled azobenzene unit into the photoagonist of α7 nicotinic acetylcholine receptors (α7 nAChRs) to investigate the effects of deuteration in photopharmacology. Spectral data indicated that deuteration maintained most of the photophysical properties of azobenzenes. The D-labeled photoagonist exhibited good control of the activity of α7 nAChRs than the prototype photoagonist. These results confirmed that deuteration is a promising strategy to improve the photopharmacological properties.

Noninvasive prenatal screening in a pregnant woman with a history of stem cell transplant from a male donor: A case report and literature review.

BACKGROUND: As a screening method, inaccuracies in noninvasive prenatal screening (NIPS) exist, which are often attributable to biological factors. One such factor is the history of transplantation. However, there are still limited reports on such NIPS cases. METHODS: We report an NIPS case of a pregnant woman who had received a stem cell transplant from a male donor. To determine the karyotype in the woman's original cell, we performed chromosome microarray analysis (CMA) on her postnatal blood and oral mucosa. To comprehensively estimate the cell-free DNA (cfDNA) composition, we further performed standard NIPS procedures on the postnatal plasma. Moreover, we reviewed all published relevant NIPS case reports about pregnant women with transplantation history. RESULTS: NIPS showed a low-risk result for common trisomies with a fetal fraction of 65.80%. CMA on maternal white blood cells showed a nonmosaic male karyotype, while the oral mucosa showed a nonmosaic female karyotype. The proportion of donor's cfDNA in postnatal plasma was 94.73% based on the Y-chromosome reads ratio. The composition of cfDNA in maternal plasma was estimated as follows: prenatally, 13.60% maternal, 65.80% donor, and 20.60% fetal/placental, whereas postnatally, 5.27% maternal and 94.73% donor. CONCLUSIONS: This study expanded our understanding of the influence of stem cell transplantation on NIPS, allowing us to optimize NIPS management for these women.

A highly sensitive and selective colorimetric aptasensor for detecting sulfadiazine in river waters based on gold nanoparticles synthesized from discarded Longan seed extract.

Gold nanoparticles (AuNPs) were extensively employed for in-situ detection sulfadiazine (SDZ) residues, yet current synthesis methods suffer from complex procedures, reagent pollution of the environment, and low particle quality. This study presents a novel synthesis method using discarded longan seed extract as a reducing agent to synthesized high-quality AuNPs, and then can be used for in-situ SDZ detection. Response surface methodology (RSM) was employed to optimize synthesis parameters, which resulted in five optimal combinations that enhanced the flexibility of synthesis. These AuNPs, ranging in size from 18.26 nm to 33.8 nm with zeta potentials from - 29.5 mV to - 14.3 mV, were successfully loaded with functional groups from longan seed extract. In the detection of SDZ, the colorimetric aptasensor demonstrated excellent sensitivity and selectivity over other antibiotics with a limit of detection and quantification at 70.98 ng·mL-1 and 236.59 ng·mL-1 in the concentration range of 200-800 ng·mL-1. Recoveries of spiked SDZ samples ranged from 97.90% to 106.7%, with RSD values below 9.25%. Meanwhile, the aptasensor exhibited exceptional diagnostic efficacy (AUC: 0.976) compared to UV absorption methods in the ROC evaluation. In conclusion, this study highlights the potential of using AuNPs synthesized from longan seed extract coupled with aptamer technology as a straightforward detection method for SDZ in river water, offering promising applications in environmental monitoring.

Reynosin protects neuronal cells from microglial neuroinflammation by suppressing NLRP3 inflammasome activation mediated by NADPH oxidase.

Neuroinflammation, mediated by the nucleotide-binding oligomerization domain-like receptor family pyrin domain-containing-3 (NLRP3) inflammasome, is a significant contributor to the pathogenesis of neurodegenerative diseases (NDDs). Reynosin, a natural sesquiterpene lactone (SL), exhibits a broad spectrum of pharmacological effects, suggesting its potential therapeutic value. However, the effects and mechanism of reynosin on neuroinflammation remain elusive. The current study explores the effects and mechanisms of reynosin on neuroinflammation using mice and BV-2 microglial cells treated with lipopolysaccharide (LPS). Our findings reveal that reynosin effectively reduces microglial inflammation in vitro, as demonstrated by decreased CD11b expression and lowered interleukin-1 beta (IL-1ß) and interleukin-18 (IL-18) mRNA and protein levels. Correspondingly, in vivo, results showed a reduction in the number of Iba-1 positive cells and alleviation of morphological alterations, alongside decreased expressions of IL-1ß and IL-18. Further analysis indicates that reynosin inhibits NLRP3 inflammasome activation, evidenced by reduced transcription of NLRP3 and caspase-1, diminished NLRP3 protein expression, inhibited apoptosis-associated speck-like protein containing a CARD (ASC) oligomerization, and decreased caspase-1 self-cleavage. Additionally, reynosin curtailed the activation of nicotinamide adenine dinucleotide phosphate (NADPH) oxidase, demonstrated by reduced NADP+ and NADPH levels, downregulation of gp91phox mRNA, protein expression, suppression of p47phox expression and translocation to the membrane. Moreover, reynosin exhibited a neuroprotective effect against microglial inflammation in vivo and in vitro. These collective findings underscore reynosin's capacity to mitigate microglial inflammation by inhibiting the NLRP3 inflammasome, thus highlighting its potential as a therapeutic agent for managing neuroinflammation.

Gut-derived metabolite 3-methylxanthine enhances cisplatin-induced apoptosis via dopamine receptor D1 in a mouse model of ovarian cancer.

Platinum-based chemotherapy failure represents a significant challenge in the management of ovarian cancer (OC) and contributes to disease recurrence and poor prognosis. Recent studies have shed light on the involvement of the gut microbiota in modulating anticancer treatments. However, the precise underlying mechanisms, by which gut microbiota regulates the response to platinum-based therapy, remain unclear. Here, we investigated the role of gut microbiota on the anticancer response of cisplatin and its underlying mechanisms. Our results demonstrate a substantial improvement in the anticancer efficacy of cisplatin following antibiotic-induced perturbation of the gut microbiota in OC-bearing mice. 16S rRNA sequencing showed a pronounced alteration in the composition of the gut microbiome in the cecum contents following exposure to cisplatin. Through metabolomic analysis, we identified distinct metabolic profiles in the antibiotic-treated group, with a notable enrichment of the gut-derived metabolite 3-methylxanthine in antibiotic-treated mice. Next, we employed a strategy combining transcriptome analysis and chemical-protein interaction network databases. We identified metabolites that shared structural similarity with 3-methylxanthine, which interacted with genes enriched in cancer-related pathways. It is identified that 3-methylxanthinesignificantly enhances the effectiveness of cisplatin by promoting apoptosis both in vivo and in vitro. Importantly, through integrative multiomics analyses, we elucidated the mechanistic basis of this enhanced apoptosis, revealing a dopamine receptor D1-dependent pathway mediated by 3-methylxanthine. This study elucidated the mechanism by which gut-derived metabolite 3-methylxanthine mediated cisplatin-induced apoptosis. Our findings highlight the potential translational significance of 3-methylxanthine as a promising adjuvant in conjunction with cisplatin, aiming to improve treatment outcomes for OC patients.IMPORTANCEThe precise correlation between the gut microbiota and the anticancer effect of cisplatin in OC remains inadequately understood. Our investigation has revealed that manipulation of the gut microbiota via the administration of antibiotics amplifies the efficacy of cisplatin through the facilitation of apoptosis in OC-bearing mice. Metabolomic analysis has demonstrated that the cecum content from antibiotic-treated mice exhibits an increase in the levels of 3-methylxanthine, which has been shown to potentially enhance the therapeutic effectiveness of cisplatin by an integrated multiomic analysis. This enhancement appears to be attributable to the promotion of cisplatin-induced apoptosis, with 3-methylxanthine potentially exerting its influence via the dopamine receptor D1-dependent pathway. These findings significantly contribute to our comprehension of the impact of the gut microbiota on the anticancer therapy in OC. Notably, the involvement of 3-methylxanthine suggests its prospective utility as a supplementary component for augmenting treatment outcomes in patients afflicted with ovarian cancer.

Light field image super-resolution based on dual learning and deep Fourier channel attention.

Light field (LF) imaging has gained significant attention in the field of computational imaging due to its unique capability to capture both spatial and angular information of a scene. In recent years, super-resolution (SR) techniques based on deep learning have shown considerable advantages in enhancing LF image resolution. However, the inherent challenges of obtaining rich structural information and reconstructing complex texture details persist, particularly in scenarios where spatial and angular information are intricately interwoven. This Letter introduces a novel, to the best of our knowledge, approach for Disentangling LF Image SR Network (DLISN) by leveraging the synergy of dual learning and Fourier channel attention (FCA) mechanisms. Dual learning strategies are employed to enhance reconstruction results, addressing limitations in model generalization caused by the difficulty in acquiring paired datasets in real-world LF scenarios. The integration of FCA facilitates the extraction of high-frequency information associated with different structures, contributing to improved spatial resolution. Experimental results consistently demonstrate superior performance in enhancing the resolution of LF images.

A New Species of Diploderma (Squamata, Agamidae) from the Valley of Dadu River in Sichuan Province, with a Redescription of Topotypes of D. splendidum from Hubei Province, China.

This study describes a novel species of Diploderma (Squamata, Agamidae) from the lower valley of the Dadu River of the Sichuan Province of Western China based on its distinct morphological features and molecular evidence. D. daduense sp. nov. can be distinguished from its congeners by its tympanum concealed; head mainly green-yellow, supplemented by black; skin folds under the nuchal and dorsal crest obviously present in adult males only, its vertebral crest discontinuous between nuchal and dorsal sections with a distinct gap; transverse gular fold present but not obvious in some individuals; gular spot absent in both sexes; dorsolateral stripes green-yellow anteriorly, cyan in the center and blurry off-white posteriorly in adult males, the upper edge of dorsolateral stripes strongly jagged in adult males; no radial stripes around the eyes; inner-lip coloration smoky-white, and the coloration of the tongue and oral cavity as a light-flesh color in life; bright green-yellow transverse stripes on dorsal body in males; black patches are evenly distributed along the vertebral line between the dorsolateral stripes from the neck to the base of the tail in males; beech-brown or gray-brown line along the vertebral line with heart-shaped or diamond-shaped black patches on the dorsal body in females; and supratemporals fewer than four on at least one side. The phylogenetic tree based on mitochondrial ND2 sequences indicates that D. daduense sp. nov. forms an independent clade with strong support 1/100 in ML bootstrap/Bayesian posterior probability and is the sister group to D. splendidum. At the inter-species level, the p-distance is at least 6.95%, further confirming that an independent species had been identified. Our work raises the number of species within the genus Diploderma to 47.

Local Mechanical Modulation-Driven Evagination in Invaginated Epithelia.

Local cells can actively create reverse bending (evagination) in invaginated epithelia, which plays a crucial role in the formation of elaborate organisms. However, the precise physical mechanism driving the evagination remains elusive. Here, we present a three-dimensional vertex model, incorporating the intrinsic cell polarity, to explore the complex morphogenesis induced by local mechanical modulations. We find that invaginated tissues can spontaneously generate local reverse bending due to the shift of the apicobasal polarity. Their exact shapes can be analytically determined by the local apicobasal differential tension and the internal stress. Our continuum theory exhibits three regions in a phase diagram controlled by these two parameters, showing curvature transitions from ordered to disordered states. Additionally, we delve into epithelial curvature transition induced by the nucleus repositioning, revealing its active contribution to the apicobasal force generation. The uncovered mechanical principles could potentially guide more studies on epithelial folding in diverse systems.

Different mechanisms underlie similar species-area relationships in two tropical archipelagoes.

Despite much research in the field of island biogeography, mechanisms regulating insular diversity remain elusive. Here, we aim to explore mechanisms underlying plant species-area relationships in two tropical archipelagoes in the South China Sea. We found positive plant species-area relationships for both coral and continental archipelagoes. However, our results showed that different mechanisms contributed to similar plant species-area relationships between the two archipelagoes. For coral islands, soil nutrients and spatial distance among communities played major roles in shaping plant community structure and species diversity. By contrast, the direct effect of island area, and to a lesser extent, soil nutrients determined plant species richness on continental islands. Intriguingly, increasing soil nutrients availability (N, P, K) had opposite effects on plant diversity between the two archipelagoes. In summary, the habitat quality effect and dispersal limitation are important for regulating plant diversity on coral islands, whereas the passive sampling effect, and to a lesser extent, the habitat quality effect are important for regulating plant diversity on continental islands. More generally, our findings indicate that island plant species-area relationships are outcomes of the interplay of both niche and neutral processes, but the driving mechanisms behind these relationships depends on the type of islands.

MMCA-NET: A Multimodal Cross Attention Transformer Network for Nasopharyngeal Carcinoma Tumor Segmentation Based on a Total-Body PET/CT System.

Nasopharyngeal carcinoma (NPC) is a malignant tumor primarily treated by radiotherapy. Accurate delineation of the target tumor is essential for improving the effectiveness of radiotherapy. However, the segmentation performance of current models is unsatisfactory due to poor boundaries, large-scale tumor volume variation, and the labor-intensive nature of manual delineation for radiotherapy. In this paper, MMCA-Net, a novel segmentation network for NPC using PET/CT images that incorporates an innovative multimodal cross attention transformer (MCA-Transformer) and a modified U-Net architecture, is introduced to enhance modal fusion by leveraging cross-attention mechanisms between CT and PET data. Our method, tested against ten algorithms via fivefold cross-validation on samples from Sun Yat-sen University Cancer Center and the public HECKTOR dataset, consistently topped all four evaluation metrics with average Dice similarity coefficients of 0.815 and 0.7944, respectively. Furthermore, ablation experiments were conducted to demonstrate the superiority of our method over multiple baseline and variant techniques. The proposed method has promising potential for application in other tasks.